PLC signal dynamic mapping method and system for flat wire motor automated production line

Abstract

The present application relates to the technical field of industrial automation control and data communication, and particularly relates to a PLC signal dynamic mapping method and system for a flat wire motor automated production line. The method comprises the following steps: analyzing a PLC symbol table, extracting data type metadata of key variables and their neighborhoods to generate a structure fingerprint vector and store the structure fingerprint vector as a reference template; reading anchor point data in a regular cycle, and triggering dynamic addressing if the real-time type identifier of the anchor point data is inconsistent with the fingerprint record; performing a sliding window matching in the vicinity of the original address, assigning neighborhood distance weights according to the principle of near large and far small to calculate a structure matching score; introducing a drift penalty factor to suppress far-end deviation, combining the matching score to calculate a final confidence, and updating a memory mapping table using the best offset if the maximum confidence exceeds a safety threshold. The scheme of the present application realizes millisecond-level self-healing after address drift by using the stability of the variable neighborhood structure, ensures data continuity and reduces maintenance costs.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control and data communication technology. More specifically, this invention relates to a PLC signal dynamic mapping method and system for automated production lines of flat wire motors. Background Technology

[0002] Flat wire motor stator production lines represent a typical high-end discrete manufacturing scenario, with control systems typically consisting of dozens of heterogeneous PLCs. To achieve digital twin production line functionality, end-to-end quality traceability, and virtual debugging, the host computer system needs to read tens of thousands of signal points from the PLCs in real time via communication protocols. Currently, data interaction between the host computer and the PLC heavily relies on static address mapping tables, where the absolute memory addresses of variables are usually explicitly defined in the system configuration. During production line construction, ramp-up testing, and multi-model changeovers, electrical engineers frequently need to modify the control logic, such as inserting new status bits or parameters before existing variables. Since PLC memory allocation is typically linear, once a new variable is inserted, the absolute addresses of all subsequent variables automatically shift, resulting in address drift. In this case, the host computer cannot automatically detect the underlying address change and continues to read data according to the old address, leading to misaligned data. This data misalignment can cause malfunctions in the digital twin model and even lead to misjudgments in quality.

[0003] To address the aforementioned issues, existing solutions primarily rely on manually re-exporting the PLC's symbol table, manually updating the host computer configuration, and restarting the service. Specifically, when data anomalies are detected or program modifications are completed, maintenance personnel need to use programming software to connect to the PLC, export the latest variable address list, identify discrepancies through comparison, and correct the mapping relationships in the host computer database or configuration file one by one. While some communication protocols that support symbolic addressing alleviate the constraints of absolute address binding to some extent, in large-scale, high-frequency data acquisition scenarios, symbolic addressing is far less efficient than absolute addressing and cannot completely avoid parsing errors caused by changes in the underlying data block structure. Therefore, in practical engineering applications, most production lines still employ efficient acquisition solutions based on absolute addresses, coupled with manual maintenance processes to handle changes.

[0004] However, this manual update method, which relies on human intervention, has significant drawbacks. In flat wire motor production lines operating continuously for 24 hours, frequent downtime for maintenance is extremely costly, and manual operation is prone to introducing new errors, severely impacting production efficiency. Specifically, the manual repair process usually lags behind the occurrence of the fault, resulting in gaps or contaminated data in the production line data during the window between program modification and mapping update, disrupting data continuity and integrity. Furthermore, as the number of production line devices increases, the maintenance workload grows exponentially, greatly increasing the pressure on the operations and maintenance team. Therefore, there is an urgent need for a dynamic mapping method that can automatically adapt to PLC address changes, enabling rapid detection and automatic repair of address drift while ensuring communication efficiency. Summary of the Invention

[0005] The purpose of this invention is to propose a dynamic mapping method and system for PLC signals in automated production lines for flat wire motors, in order to solve the problems in the prior art where variable address drift is caused by PLC program modification in automated production lines for flat wire motors, which leads to data acquisition errors in the host computer and high manual maintenance costs; to this end, this invention provides solutions in the following two aspects.

[0006] In a first aspect, the present invention provides a PLC signal dynamic mapping method for automated production lines of flat wire motors, comprising: The PLC symbol table is parsed, a neighborhood window for key variables is defined, and the data type metadata of all variables within the neighborhood window is extracted to generate a structural fingerprint vector. This structural fingerprint vector is then stored as a baseline template. Preset anchor point data is read during a regular acquisition cycle, and the data type identifier in the returned message is parsed. If the data type identifier of the anchor point data is inconsistent with the corresponding reference data type metadata in the baseline template, an address drift is determined to have occurred in the memory region where the key variable is located, triggering a dynamic addressing process. Sliding window matching is performed within the search interval near the original baseline address of the key variable, allocating neighborhood distance weights according to the principle of larger values ​​for closer objects and smaller values ​​for farther objects, and calculating the structural matching score at the candidate address offset. A drift penalty factor is introduced to suppress far-end offsets, and the final confidence of the candidate offset is calculated based on the structural matching score. The search interval is traversed to find the optimal offset with the highest final confidence. If the final confidence of the optimal offset is greater than a preset safety threshold, the mapping address of the key variable is updated using the optimal offset.

[0007] Preferably, in the step of parsing the PLC symbol table and defining the neighborhood window of the key variable, the radius of the neighborhood window is N, and the structural fingerprint vector includes the data type metadata of the key variable and its N adjacent variables before and after it; the baseline template is persistently stored in the fingerprint database of the middleware.

[0008] By defining a neighborhood window with a radius of N and persistently storing the fingerprint, the beneficial effect is that it constructs a context feature with local uniqueness for each key variable, effectively solving the problem of high repetition rate and difficulty in locating single simple data types (such as Boolean or integer) in memory, and providing reliable benchmark data for subsequent accurate relocation.

[0009] Preferably, in the step of reading preset anchor point data during a regular acquisition cycle, the anchor point data is selected from variables with typical data characteristics at the head, middle, and tail of the data block where the key variable is located; the triggering of the dynamic addressing process includes pausing normal acquisition and starting a scan of the buffer near the original reference address.

[0010] By adopting an anchor point monitoring mechanism and selecting typical variables at the head, middle, and tail of the data block as sentinels, the beneficial effect is that it can detect changes in the underlying memory structure in real time with extremely low system overhead without affecting normal communication efficiency. At the same time, the mechanism of pausing normal data collection after triggering avoids the input of erroneous data and ensures the data quality of upper-layer applications (such as digital twins).

[0011] Preferably, in the step of calculating the structure matching score at the candidate address offset, the calculation logic of the structure matching score is as follows: multiply and sum the reciprocal of the weight normalization factor, the neighborhood distance weight of each position in the neighborhood window, and the type impulse function; wherein, the type impulse function is used to characterize the consistency between the real-time data type identifier and the reference data type metadata, and the neighborhood distance weight is related to the distance of the variable relative to the key variable in the neighborhood window.

[0012] Preferably, the value of the neighborhood distance weight is equal to the reciprocal of the absolute value of the distance between the variable and the key variable plus one; the value of the weight normalization factor is equal to the sum of the neighborhood distance weights of all variables within the neighborhood window.

[0013] By introducing a neighborhood distance weight that follows the principle of larger values ​​for closer variables and smaller values ​​for farther variables to calculate the structure matching score, the beneficial effect is that it conforms to the rule in engineering practice that program modifications usually preserve the integrity of local structures, so that the neighbors of the key variables have a greater impact on the matching results, thereby improving the robustness of identifying key variables in complex memory environments.

[0014] Preferably, in the step of calculating the final confidence of the candidate offset by combining the structure matching score, the calculation logic of the final confidence is as follows: the final confidence is obtained by multiplying the structure matching score, the distance attenuation term, and the scaling constant; wherein, the value of the distance attenuation term is equal to the reciprocal of the product of the drift penalty factor and the absolute physical distance of the candidate offset.

[0015] Preferably, the drift penalty factor is an empirical constant; the step of updating the mapping address of the key variable using the optimal offset specifically includes: adding the optimal offset to the original base address to obtain a new address, and using the new address to update the memory mapping table of the host computer.

[0016] Introducing a drift penalty factor and a distance decay term to calculate the final confidence level has the advantage of combining spatial physical constraints to suppress pseudo-targets that are structurally similar but too far from the original address, effectively filtering out interference from similar structures that occasionally appear in memory, and ensuring the high confidence level of the final selected new address.

[0017] Preferably, after the step of traversing the search interval to find the optimal offset with the highest final confidence, the method further includes: if the final confidence of the optimal offset is less than or equal to the security threshold, then dynamic addressing is determined to have failed and an alarm is issued.

[0018] Setting up security thresholds and alarm mechanisms provides a safety net for automatic addressing, preventing forced mapping updates when program refactoring leads to generally low matching rates. This avoids more serious system failures caused by incorrect mappings and ensures system security.

[0019] Preferably, the specific logic of the type impulse function is as follows: if the real-time data type identifier is consistent with the reference data type metadata, the function returns a value of one; otherwise, the function returns a value of zero; the real-time data type identifier is obtained by parsing the type header information in the underlying communication protocol message.

[0020] In the second aspect, a PLC signal dynamic mapping system for an automated production line of flat wire motors includes: The processor; the memory storing computer instructions for dynamic mapping of PLC signals for automated production lines of flat wire motors, which, when executed by the processor, cause the system to perform the aforementioned dynamic mapping method for PLC signals in automated production lines of flat wire motors.

[0021] The beneficial effects of this invention are as follows: By constructing a fingerprint database based on a neighborhood structure, combined with real-time anchor point monitoring and a weighted sliding search algorithm, the problem of data acquisition interruption caused by PLC address drift in flat wire motor production lines is solved. This enables automatic repair and seamless switching of address mapping relationships without manual intervention by utilizing the relative stability of the local structure of variables. Moreover, through dual constraint evaluation of structural similarity and spatial distance attenuation, addressing accuracy in complex memory environments is guaranteed, while maintenance costs are significantly reduced, and the adaptive capability and operational stability of the automated production line data acquisition system are significantly improved. Attached Figure Description

[0022] Figure 1 This schematically illustrates the steps of the PLC signal dynamic mapping method for the flat wire motor automated production line in this embodiment; Figure 2 This is a structural fingerprint search and matching analysis diagram after address drift in this invention; Figure 3 This is a comparison chart of mapping reliability and data validity in this invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0024] like Figure 1 As shown, the PLC signal dynamic mapping method for automated production lines of flat wire motors in this embodiment includes the following steps: Step S1: Parse the PLC symbol table, define the neighborhood window of the key variables, extract the data type metadata of all variables in the neighborhood window to generate a structural fingerprint vector, and store the structural fingerprint vector as a reference template.

[0025] Specifically, in practical applications, isolated variables such as integer values ​​often lack sufficient distinctiveness in memory. However, when an integer variable is immediately preceded by two boolean variables and immediately followed by a real variable, this combination structure possesses extremely high uniqueness. Therefore, this step first parses the PLC's source program symbol table or DB block definition file during the system initialization phase. For critical variables that require close monitoring, the system will define their neighborhood radius N, with a recommended value of 3, meaning that three variables are selected before and after the critical variable.

[0026] Subsequently, the system extracts the data type metadata of all variables within the window and generates a structural fingerprint vector. For example, the fingerprint content can be a sequence composed of Bool, Bool, WORD (as the target), Real, Int, DInt, and Byte. This fingerprint will be persistently stored as a baseline template in the middleware's fingerprint database.

[0027] In this way, by constructing a variable neighborhood structure fingerprint database, the system establishes a robust identification feature by utilizing the local uniqueness of variable type sequences, effectively overcoming the problem of insufficient features of a single variable, and laying a solid data foundation for retrieving key variables in the event of address chaos.

[0028] Step S2: Read the preset anchor point data during the normal acquisition cycle, parse the data type identifier in the returned message, and if the data type identifier of the anchor point data is inconsistent with the corresponding reference data type metadata in the baseline template, it is determined that the memory area where the key variable is located has experienced address drift and the dynamic addressing process is triggered.

[0029] To avoid consuming significant communication resources by performing a full real-time search on all variables, the system employs a lightweight sentinel mechanism. Specifically, three to five variables with typical data characteristics are selected as anchor points at the beginning, middle, and end of key data blocks. During regular acquisition cycles, the system prioritizes sending PLC read commands to obtain data at the anchor point locations and parses the data type identifiers in the returned messages, such as the Transport Size field in the S7 protocol header. If the data type of the read anchor point differs from the record in the fingerprint database—for example, the original address should be a Real floating-point number but cannot be parsed in this format or is identified as a Byte type—the system determines that the data block has experienced address drift, immediately suspends normal acquisition, and triggers the subsequent dynamic addressing process.

[0030] This real-time drift detection and dynamic addressing triggering method, which uses a small number of anchor points as probes, can keenly detect changes in the underlying program without affecting normal communication efficiency, thereby ensuring that the system has the ability to respond quickly to abnormal states.

[0031] Step S3: Perform sliding window matching within the search interval near the original reference address of the key variable, assign neighborhood distance weights according to the principle of larger values ​​for closer objects and smaller values ​​for farther objects, and calculate the structure matching score at the candidate address offset.

[0032] Once the addressing process is triggered, the system will perform a sliding window matching within a buffer of about 100 bytes before and after the original address. During the comparison, the principle of "nearest is larger, farthest is smaller" applies, meaning that neighboring variables closer to the key variable have higher reference value for their structural features and therefore receive greater weight.

[0033] Specifically, calculate the candidate address offset. Structural matching score at the location Its expression is as follows: ; In the formula, This represents the candidate search step size in bytes, within the preset search range. Inner traversal; This is the original base address of the key variable in the old mapping table; Indicates the first The relative address distance of each neighbor variable to the key variable; This indicates the real-time memory type identifier obtained by parsing the underlying communication protocol messages; For the first stored in the database Metadata of reference types for each neighbor; For type impulse function, when the real-time type With reference type If the match is consistent, the function returns 1; otherwise, it returns 0.

[0034] also, The neighborhood distance weight is expressed as follows: ; According to the above expression, the key variable has a weight of 1, and the adjacent variable has a weight of 0.5. This setting reflects the physical law that program modifications usually sever distant connections but tend to preserve near-terminal structures.

[0035] Finally, the weight normalization factor The expression is: .

[0036] In the structural feature matching process, an inverse proportional distance weight based on index bias is introduced. and combined Strict normalization not only effectively eliminates the drift in scoring dimensions caused by different neighborhood window spans, but also accurately maps the software engineering principle of "strong coupling of near-end variables and weak correlation of far-end variables" in the underlying control flow data structure.

[0037] The following is a specific calculation example to illustrate the application process of this algorithm.

[0038] Assume the neighborhood radius N is 1, meaning only the key variable and its one preceding and one following neighbor variable are considered. Calculate the weights using the aforementioned formula, when... A value of 0 indicates that the weight of a key variable is 1, while when... A value of -1 or 1 indicates that the weights of the preceding and following neighbors are both 0.5, therefore the weight normalization factor is... The calculation result is 2.

[0039] Based on this, assume the original base address of the key variable. The value is 100, and the reference fingerprint is stored in the database. The sequences are Bool, Int, and Real, respectively corresponding to... Take the positions of -1, 0, and 1.

[0040] First, try searching for the offset. The case is 2. The system reads the memory type near address 102. Suppose that the type sequence read at this point is also Bool, Int, Real, indicating that the types at all positions are completely consistent, and the corresponding impulse function results are all 1. The weighted sum is then 2, leading to the final matching score at that offset. The result is 1.0.

[0041] Then examine the search offset. In the case of a value of 4, suppose the type sequence read becomes Byte, Int, Real. A comparison reveals that at position... for At this point, the reference type Boolean does not match the actual byte read, resulting in an impulse function value of 0, while the other two points still match. The weighted sum then becomes 1.5, and the final structure matching score at this offset is calculated. The result was 0.75.

[0042] Thus, by using the optimal offset search based on weighted structural similarity, we can quantitatively evaluate the similarity between each potential location and the original structure, effectively filter out the most likely candidate addresses from the background data, and provide a reasonable assessment even when some neighborhood structures are damaged.

[0043] Step S4: Introduce a drift penalty factor to suppress far-end offsets, calculate the final confidence of candidate offsets based on the structure matching score, traverse the search interval to find the best offset with the highest final confidence, and if the final confidence of the best offset is greater than a preset safety threshold, then use the best offset to update the mapping address of the key variable.

[0044] Considering that multiple structurally similar regions may exist in memory, this method introduces the principle of least action to effectively eliminate interference and pinpoint the most accurate new address. This principle states that the smaller the offset, the greater the probability that it is the correct address. Based on this principle, candidate offsets... Final confidence level The methods for obtaining it are as follows: ; in, Offset The posterior probability of the true offset value; The absolute physical distance of the candidate offset, in bytes. The drift penalty factor is defined in bytes. -1 This is used to suppress interference from similar structures at distant ends; in this embodiment, its value is 0.1. This is a scaling constant with a value of 1.0.

[0045] In the above confidence level calculation, the present invention introduces a method that includes absolute displacement. The nonlinear penalty term integrates its underlying logic with the "minimum action principle" of heuristic search and the prior decay model of Bayesian posterior probability. Its core guiding principle is: when structural similarity is the same, prioritize assigning higher posterior confidence to mapping blocks that are physically closer in memory. This mechanism effectively overcomes the interference of far-end pseudo-matches caused by a large amount of homogeneous data (such as consecutive Boolean placeholders) in PLC memory, thereby significantly enhancing the robustness and success rate of dynamic addressing.

[0046] After the calculation is complete, the system will iterate through all... The goal is to find the optimal offset that maximizes the final confidence level. Then, the optimal confidence level is compared with a preset safety threshold. The comparison is performed, and this security threshold is typically set to 0.80. If the confidence level is greater than this security threshold, the addressing is considered successful, and the new address is... Update to the original base address With the optimal offset The sum of the values; if the confidence level is less than the security threshold, the addressing is deemed to have failed and an alarm mechanism is triggered.

[0047] Continuing with the example scenario in step S3, let's assume a drift penalty factor. Set to 0.1, scaling constant. Set it to 1. First, calculate the offset. The confidence level is 2. The previously obtained structure matching score is known. Given a confidence level of 1.0, the confidence level can be calculated based on the expression for the final confidence level. It is 0.833.

[0048] Then examine the offset. For the far-end case of 20, assume that the memory structure at that location also happens to be a perfect match, resulting in a structure matching score of 20. The initial value is also 1.0. Substituting this into the final confidence expression again, the increased distance leads to a higher final confidence score. It dropped significantly to 0.333.

[0049] It can be seen that although the two have the same structural matching degree, due to The greater the distance, the lower the confidence level, and the system will prioritize selecting [the appropriate option]. This serves as the optimal offset. This comparison intuitively demonstrates how the algorithm utilizes a distance penalty mechanism to suppress far-end interference, prioritizing the correct address with the closer physical distance when matching scores are the same. The following section, in conjunction with the appendix... Figure 2 and attached Figure 3 The beneficial effects of the present invention will be further explained.

[0050] Figure 2This is a graph showing the structural fingerprint search and matching analysis after address drift in this invention. The horizontal axis represents the memory address search offset, and the vertical axis represents the fingerprint structure matching score. As can be seen from the graph, a very significant isolated spike appears at the offset of +12, with a score approaching 1.0, while the scores at other positions are effectively suppressed. This indicates that the algorithm can accurately locate the new address after drift, and the distance weighting mechanism effectively eliminates false matches.

[0051] Figure 3 This is a comparison chart of mapping reliability and data validity in this invention. The chart shows a scenario simulating a PLC program change at 40 seconds into the production run. The curve corresponding to the prior art shows that the reliability instantly drops to 0 after the change occurs, causing data acquisition to be interrupted; while the curve corresponding to this invention only shows a very brief dip before quickly rebounding, indicating that the system has millisecond-level address self-healing capability, ensuring the continuity and integrity of production line data.

[0052] Thus, by combining confidence assessment based on distance decay with adaptive mapping updates, content similarity and spatial proximity are combined to effectively eliminate false matches, ensuring that the final selected new address has extremely high credibility and achieving automated mapping repair.

[0053] This invention also provides a PLC signal dynamic mapping system for an automated production line of flat wire motors. The system includes a processor and a memory, the memory storing computer program instructions. When the processor executes the computer program instructions, it implements the PLC signal dynamic mapping method for an automated production line of flat wire motors according to the present invention.

[0054] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and therefore will not be described in detail here.

[0055] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented by computer-readable / executable instructions stored or otherwise maintained on such a computer-readable medium.

[0056] In the description of this specification, "multiple" means at least two, such as two, three or more, etc., unless otherwise expressly and specifically defined.

[0057] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention.

Claims

1. A method for dynamic mapping of PLC signals in automated production lines for flat wire motors, characterized in that, include: Parse the PLC symbol table, define the neighborhood window of key variables, extract the data type metadata of all variables in the neighborhood window to generate a structural fingerprint vector, and store the structural fingerprint vector as a reference template; During the regular acquisition cycle, preset anchor data is read, and the data type identifier in the returned message is parsed. If the data type identifier of the anchor data is inconsistent with the corresponding reference data type metadata in the baseline template, it is determined that the memory area where the key variable is located has experienced address drift and a dynamic addressing process is triggered. The anchor data is selected from the head, middle and tail of the data block where the key variable is located, and the variables with typical data characteristics. Triggering the dynamic addressing process involves pausing normal data acquisition and initiating a scan of the search range near the original reference address; Sliding window matching is performed within the search interval near the original reference address of the key variable, and neighborhood distance weights are assigned according to the principle of larger values ​​for closer and smaller values ​​for farther, and the structural matching score at the candidate address offset is calculated. A drift penalty factor is introduced to suppress far-end offsets. The final confidence of the candidate offsets is calculated by combining the structure matching score. The search interval is traversed to find the best offset with the highest final confidence. If the final confidence of the best offset is greater than a preset safety threshold, the mapping address of the key variable is updated using the best offset. In the step of calculating the final confidence of the candidate offset by combining the structure matching score, the calculation logic of the final confidence is as follows: multiply the structure matching score, the distance attenuation term, and the scaling constant to obtain the final confidence. The distance decay term is equal to the reciprocal of the product of the drift penalty factor and the absolute physical distance of the candidate offset.

2. The PLC signal dynamic mapping method for automated production lines of flat wire motors according to claim 1, characterized in that, In the step of parsing the PLC symbol table and defining the neighborhood window of the key variable, the radius of the neighborhood window is N, and the structural fingerprint vector includes the data type metadata of the key variable and its N adjacent variables before and after it; the baseline template is persistently stored in the fingerprint database of the middleware.

3. The PLC signal dynamic mapping method for automated production lines of flat wire motors according to claim 1, characterized in that, In the step of calculating the structure matching score at the candidate address offset, the calculation logic of the structure matching score is as follows: multiply and sum the reciprocal of the weight normalization factor, the neighborhood distance weight of each position in the neighborhood window, and the type impulse function. The type impulse function is used to characterize the consistency between the real-time data type identifier and the reference data type metadata, and the neighborhood distance weight is related to the distance of the variable relative to the key variable within the neighborhood window.

4. The PLC signal dynamic mapping method for automated production lines of flat wire motors according to claim 3, characterized in that, The value of the neighborhood distance weight is equal to the reciprocal of the absolute value of the distance of the variable relative to the key variable plus one; the value of the weight normalization factor is equal to the sum of the neighborhood distance weights of all variables within the neighborhood window.

5. The PLC signal dynamic mapping method for automated production lines of flat wire motors according to claim 1, characterized in that, The drift penalty factor is an empirical constant; the step of updating the mapping address of the key variable using the optimal offset specifically includes: adding the optimal offset to the original base address to obtain a new address, and using the new address to update the memory mapping table of the host computer.

6. The PLC signal dynamic mapping method for automated production lines of flat wire motors according to claim 1, characterized in that, After the step of traversing the search interval to find the optimal offset with the highest final confidence, the method further includes: if the final confidence of the optimal offset is less than or equal to the safety threshold, then dynamic addressing is determined to have failed and an alarm is issued.

7. The PLC signal dynamic mapping method for automated production lines of flat wire motors according to claim 3, characterized in that, The specific logic of the type impulse function is as follows: if the real-time data type identifier is consistent with the reference data type metadata, the function returns one; otherwise, the function returns zero. The real-time data type identifier is obtained by parsing the type header information in the underlying communication protocol message.

8. A PLC signal dynamic mapping system for automated production lines of flat wire motors, characterized in that, include: processor; A memory storing computer instructions for dynamic mapping of PLC signals in an automated production line for flat wire motors, wherein when the computer instructions are executed by the processor, the system performs the PLC signal dynamic mapping method for an automated production line for flat wire motors according to any one of claims 1-7.

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